mlr_tuners_irace: Hyperparameter Tuning with Iterated Racing.

In mlr-org/mlr3tuning: Hyperparameter Optimization for 'mlr3'

Hyperparameter Tuning with Iterated Racing.

Description

Subclass for iterated racing. Calls irace::irace() from package irace.

Dictionary

This Tuner can be instantiated with the associated sugar function tnr():

tnr("irace")

Control Parameters

n_instances

integer(1)
Number of resampling instances.

For the meaning of all other parameters, see irace::defaultScenario(). Note that we have removed all control parameters which refer to the termination of the algorithm. Use bbotk::TerminatorEvals instead. Other terminators do not work with TunerIrace.

Archive

The ArchiveBatchTuning holds the following additional columns:

• "race" (integer(1))
  Race iteration.

• "step" (integer(1))
  Step number of race.

• "instance" (integer(1))
  Identifies resampling instances across races and steps.

• "configuration" (integer(1))
  Identifies configurations across races and steps.

Result

The tuning result (instance$result) is the best-performing elite of the final race. The reported performance is the average performance estimated on all used instances.

Progress Bars

⁠$optimize()⁠ supports progress bars via the package progressr combined with a bbotk::Terminator. Simply wrap the function in progressr::with_progress() to enable them. We recommend to use package progress as backend; enable with progressr::handlers("progress").

Logging

All Tuners use a logger (as implemented in lgr) from package bbotk. Use lgr::get_logger("bbotk") to access and control the logger.

Optimizer

This Tuner is based on bbotk::OptimizerBatchIrace which can be applied on any black box optimization problem. See also the documentation of bbotk.

Resources

There are several sections about hyperparameter optimization in the mlr3book.

• Getting started with hyperparameter optimization.

• An overview of all tuners can be found on our website.

• Tune a support vector machine on the Sonar data set.

• Learn about tuning spaces.

• Estimate the model performance with nested resampling.

• Learn about multi-objective optimization.

• Simultaneously optimize hyperparameters and use early stopping with XGBoost.

• Automate the tuning.

The gallery features a collection of case studies and demos about optimization.

• Learn more advanced methods with the Practical Tuning Series.

• Learn about hotstarting models.

• Run the default hyperparameter configuration of learners as a baseline.

• Use the Hyperband optimizer with different budget parameters.

The cheatsheet summarizes the most important functions of mlr3tuning.

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerBatch -> mlr3tuning::TunerBatchFromOptimizerBatch -> TunerBatchIrace

Methods

Public methods

• TunerBatchIrace$new()

• TunerBatchIrace$optimize()

• TunerBatchIrace$clone()

Inherited methods

• mlr3tuning::Tuner$format()
• mlr3tuning::Tuner$help()
• mlr3tuning::Tuner$print()

---

Method new()

Creates a new instance of this R6 class.

Usage

TunerBatchIrace$new()

---

Method optimize()

Performs the tuning on a TuningInstanceBatchSingleCrit until termination. The single evaluations and the final results will be written into the ArchiveBatchTuning that resides in the TuningInstanceBatchSingleCrit. The final result is returned.

Usage

TunerBatchIrace$optimize(inst)

Arguments

inst

(TuningInstanceBatchSingleCrit).

Returns

data.table::data.table.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

TunerBatchIrace$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Source

Lopez-Ibanez M, Dubois-Lacoste J, Caceres LP, Birattari M, Stuetzle T (2016). “The irace package: Iterated racing for automatic algorithm configuration.” Operations Research Perspectives, 3, 43–58. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1016/j.orp.2016.09.002")}.

See Also

Other Tuner: Tuner, mlr_tuners, mlr_tuners_cmaes, mlr_tuners_design_points, mlr_tuners_gensa, mlr_tuners_grid_search, mlr_tuners_internal, mlr_tuners_nloptr, mlr_tuners_random_search

Examples

# retrieve task
task = tsk("pima")

# load learner and set search space
learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE))

# runtime of the example is too long

# hyperparameter tuning on the pima indians diabetes data set
instance = tune(
  tuner = tnr("irace"),
  task = task,
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  term_evals = 200
)

# best performing hyperparameter configuration
instance$result

# all evaluated hyperparameter configuration
as.data.table(instance$archive)

# fit final model on complete data set
learner$param_set$values = instance$result_learner_param_vals
learner$train(task)

mlr-org/mlr3tuning documentation built on April 14, 2025, 1 a.m.